There's a lot that artificial intelligence can do, but understanding human behavior isn't one of the strong suits. A team at MIT's Computer Science and Artificial Intelligence Laboratory wants to change that. Researchers essentially turned computers into couch potatoes by feeding them hundreds of hours of footage from popular TV shows like "The Office," "Scrubs," and "Desperate Housewives," NPR reported Tuesday. Each clip ends with one of four actions: a hug, a kiss, a high five, or a handshake. Predict which one is about to happen.

If you've ever learned to program, you've probably written a Fizz Buzz test. With Fizz Buzz, you print the numbers from 1 to 100, except if it is divisible by 3, you print "fizz"; if it's divisible by 5, you print "buzz"; and if it's divisible by 15 you print "fizzbuzz." This trivial coding problem is typically achieved with a couple of if statements and checking whether each number can be divided by 3 or 5. In his recent blog post "Fizz Buzz in TensorFlow," Grus imagines he's asked to solve Fizz Buzz as part of a job interview. But instead of taking the obvious approach, he uses TensorFlow, the open-source machine learning library developed by Google.

So everyone in China has been following the European Championship in France, and this time it's the game between France and Iceland that made a lot of noise, generating 2.8 billion media impressions! It was fascinating for Chinese people as they really admired this team from Iceland. They discovered that Iceland is a country of only 330 thousand people, which is just a city for them. Shanghai for example has 25 million inhabitants! For a country so small to reach that stage of a Soccer Championship was just amazing for them.

Dr. Arno Candel, Chief Architect, H20.ai In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will show live demos of how to train sophisticated machine learning models on large datasets to solve common business problems. He will show how data scientists and application developers can use modern software tools to build smarter applications, and how to take them to production. He will present customer use cases from verticals including insurance, fraud, churn, fintech and marketing.

Deep learning, a new and growing area of machine learning, is widely regarded as an important step forward on the path toward true artificial intelligence. Tech firms such as Facebook and Google, as well as companies like Bloomberg (which focuses on financial technology and information), are already beginning to incorporate the technology into their product development. And while the exact definition of deep learning is still somewhat fluid, opportunities are growing for experts who can apply the technology to everything from speech recognition to bioinformatics, drug discovery and equities trading. The global analytics firm SAS says deep learning trains computers to perform human-like tasks such as recognizing speech, identifying images or making predictions. "Deep learning can break patterns into sub-components and build models very accurately," Wayne Thompson, the chief data scientist of SAS Data Science Technologies in Cary, N.C., explained.

Theano has more code available online in general (all though the amount of Tensorflow code/tutorials is rapidly increasing), if you learn one you should be able to pick up the other without much hassle.

Note: I posted this question to stackoverflow as well. Support Vector Machines, k-Neighbors Classifiers, Neural Networks, Decision Trees, ...) on the same training set and collects a bunch of performance metrics for each model. Now, most of these are your standard run-of-the-mill metrics like precision, recall, overall accuracy and all that, but some are more complex (or should I say "different"?), for example: I want to find a good way of ranking these models based on user-specified weights for a subset of the aforementioned performance metrics. If a user's goal was to find the model that was least "complex" while still achieving reasonable precision, they would likely assign a higher weight to the "no. of preprocessing steps" attribute and see which model gets ranked highest (probably model 2, but it really depends on the concrete values of the weights of course). So, in short, I am faced with a so-called Multiple-criteria decision-making (MCDM) problem, and I need to solve it.

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Sophisticated chatbots have, in no uncertain terms, emerged as a reality that businesses and consumers must prepare for. The media is all over this one. At the same time, looking at the spaces where these bots operate, the number of separate messaging programs with active users in (or headed into) the billions has created a fragmented marketplace. At the top tier there's WeChat, QQ, WhatsApp, Facebook Messenger, Apple iMessage, Skype, and Viber, with SnapChat, Line, Slack, Kik, Google, and Samsung holding the potential to join the billion user club if and when they see fit to make it a priority. There are hundreds of messaging apps with small and mid-sized user bases, not to mention the vast audiences that prefer SMS, email, and services like Twitter for communication.

The SaaS Co. is a Berlin-made company that scales and executes sales for B2B SaaS products with the help of deep learning. At TSC we find, contact, qualify, and set appointments with decision makers in order to hand them over to you. In this lecture-workshop-breakfast, you will learn how to win as a customer, enterprises like Microsoft, or startups like Twilio. On the other hand we will show you how to use Artificial Intelligence in a practical way in your daily sales processes.